4 results about "Service placement" patented technology

A method for an energy-efficient service placement in a mobile edge cloud comprising at least one edge site is disclosed. The method comprises receiving a service placement request from a service provider. The method comprises identifying a set of candidate edge site groups and calculating a first energy efficiency value for each identified candidate edge site group. Further, the method comprises calculating a second energy efficiency value for components of the cellular network that are involved in the communication between the user device and the edge site. The method comprises determining for each candidate edge site group, an energy efficiency metric for deploying said service placement request in a traffic path of the cellular network based on the first and second energy efficiency values. The method further comprises determining a service placement policy for the service placement based on the calculated energy efficiency metric and the obtained performance parameters.

A method for an energy-efficient service placement in a mobile edge cloud comprising at least one edge site is disclosed. The method comprises receiving a service placement request from a service provider. The method comprises identifying a set of candidate edge site groups and calculating a first energy efficiency value for each identified candidate edge site group. Further, the method comprises calculating a second energy efficiency value for components of the cellular network that are involved in the communication between the user device and the edge site. The method comprises determining for each candidate edge site group, an energy efficiency metric for deploying said service placement request in a traffic path of the cellular network based on the first and second energy efficiency values. The method further comprises determining a service placement policy for the service placement based on the calculated energy efficiency metric and the obtained performance parameters.

The invention belongs to the technical field of wireless communication, and discloses a marginal air-ground network optimization method and system based on hierarchical deep reinforcement learning (joint optimization method and system), and the optimization of the marginal air-ground network is realized through joint optimization of a service placement strategy, UAV trajectory planning, access control, bandwidth allocation and transmission power. And efficient equipment connection and data processing services are provided for disaster area rescue, urban intelligent monitoring and other scenes. According to the method, a dual-time-scale optimization strategy is adopted: on a coarse-grained frame-level time scale, a high-level agent adopts a deep Q network (DQN) proxy, and a placement strategy of service in an MEC server is decided according to a UAV task request to ensure that resource constraints are met; on the fine-grained time slot level time scale, a low-layer agent adopts an improved depth deterministic policy gradient (IDDPG) for proxy, the UAV trajectory, TDMA-based access control, bandwidth allocation proportion and transmission power are optimized, and the real-time task requirement is met. Through the layered framework, the system can improve the task success rate, the energy efficiency and the resource scheduling fairness in a dynamic environment, and efficient and stable air-ground integrated network service is realized.

This disclosure describes techniques and mechanisms for utilizing affinity routing in SDWAN networks. The techniques may enable network administrators to assign and / or configure affinity numbers to hub(s) and / or gateway(s), tunneling interface(s), service(s), etc., as well as affinity-preference-order(s) to edge device(s) within the network. Network administrators may also configure control polic(ies). The techniques enable a scalable and simplified way to automatically load-balance traffic across different gateways within a network, while reducing network resource usage. The techniques may utilize routing affinity to achieve a variety of networking related functionalities, including automatic load-balancing of traffic, provisioning of active and backup gateways, optimal route distribution to routers from routing controllers, optimized service placement for edge routers, without the need for any policy configuration at all, let alone complex policies.